Query processing using data clean rooms

ABSTRACT

Disclosed herein are methods and systems for secure data comparison using data clean rooms. In an embodiment, a computer system generates a replica database based on a provider database, which stores a cross reference table that cross references a client dataset of a client database and a provider dataset of the provider database. The system receives, at the replica database, a table that is generated by the client database using the cross-reference table. The system transmits, from the replica database, the table to the provider database. The system receives, at the replica database, a results dataset that is generated by the provider database by applying a database statement to the provider database using the table generated by the client database. The system shares, from the replica database, the results dataset with the client database.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. patent application Ser. No.18/162,701, filed Jan. 31, 2023, which is a Continuation of U.S. patentapplication Ser. No. 17/932,610, filed Sep. 15, 2022 and issued as U.S.Pat. No. 11,620,409, which is a Continuation of U.S. patent applicationSer. No. 17/644,722, filed Dec. 16, 2021 and issued as U.S. Pat. No.11,468,195, which is a Continuation of U.S. patent application Ser. No.17/463,293, filed Aug. 31, 2021 and issued as U.S. Pat. No. 11,222,141,which is a Continuation of U.S. patent application Ser. No. 17/334,297,filed May 28, 2021 and issued as U.S. Pat. No. 11,138,340, which claimspriority to U.S. Provisional Patent Application Ser. No. 63/201,489,filed Apr. 30, 2021, the contents of which are incorporated herein byreference in their entireties for all purposes.

TECHNICAL FIELD

The present disclosure generally relates to securely analyzing datausing a data clean room across different clouds and regions.

BACKGROUND

Currently, most digital advertising is performed using third-partycookies. Cookies are small pieces of data generated and sent from a webserver and stored on the user's computer by the user's web browser thatare used to gather data about customers' habits based on their websitebrowsing history. Because of privacy concerns, the use of cookies isbeing restricted. Companies may want to create target groups foradvertising or marketing efforts for specific audience segments. To doso, companies may want to compare their customer information with thatof other companies to see if their customer lists overlap for thecreation of such target groups. Thus, companies may want to perform dataanalysis, such as an overlap analysis, of their customers or other data.

BRIEF DESCRIPTION OF THE DRAWINGS

Various ones of the appended drawings merely illustrate exampleembodiments of the present disclosure and should not be considered aslimiting its scope.

FIG. 1 illustrates an example computing environment in which anetwork-based database system can implement streams on shared databaseobjects, according to some example embodiments.

FIG. 2 is a block diagram illustrating components of a compute servicemanager, according to some example embodiments.

FIG. 3 is a block diagram illustrating components of an executionplatform, according to some example embodiments.

FIG. 4 is a block diagram illustrating accounts in a database system,according to some example embodiments.

FIG. 5 is a block diagram illustrating a data clean room, according tosome example embodiments.

FIG. 6 is a block diagram illustrating a double-blind data clean room,according to some example embodiments.

FIGS. 7A-7C show data architectures for implementing a clean room acrossdifferent clouds and regions, according to some example embodiments.

FIG. 8 shows a flow diagram of a method for implementing the clean roomacross different clouds and regions, according to some exampleembodiments.

FIG. 9 illustrates a diagrammatic representation of a machine in theform of a computer system within which a set of instructions may beexecuted for causing the machine to perform any one or more of themethodologies discussed herein, in accordance with some embodiments ofthe present disclosure.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of variousembodiments of the inventive subject matter. It will be evident,however, to those skilled in the art, that embodiments of the inventivesubject matter may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail.

As discussed, companies may want to perform analysis on their customers,such as overlap analysis. To perform such types of data analyses,companies can use “trusted” third parties, who can access data from eachof the companies and perform the data analysis. However, thisthird-party approach suffers from significant disadvantages (e.g.,delay, network overhead, loss of control of customer data to the thirdparty, the analysis is performed by the third party and not therequesting party). Further, it is difficult to query data from differentparties where the parties use different virtual cloud providers andadditionally may be located in different geographic regions.

Embodiments of the present disclosure may provide a data clean roomallowing secure data analysis across multiple accounts, without the useof third parties. Each account may be associated with a differentcompany or party. The data clean room may provide privacy and securityusing secure functions to safeguard sensitive information. For example,the data clean room may restrict access to data in other accounts. Thedata clean room may also restrict which data may be used in the analysisand may restrict the output. The data clean room can be implementedbetween provider and requestor (client) accounts that are in differentregions and/or different on different cloud providers. A client accountcan request a data clean room from a provider account, and the provideraccount can create a replica of the provider account in the environmentas the client account (e.g., same cloud, same region). In some exampleembodiments, not all data is replicated from the provider primaryaccount to the provider's replica. Instead, the client account creates atable for querying and one or more queries that reference the client'stables for the queries. The tables for querying the queries can beshared to the replica and replicated to the provider account forprocessing of the queries using anonymized cross referenced data andshared functions for efficient completion of clean room queries acrossdifferent clouds and regions.

FIG. 1 illustrates an example shared data processing platform 100implementing secure messaging between deployments, in accordance withsome embodiments of the present disclosure. To avoid obscuring theinventive subject matter with unnecessary detail, various functionalcomponents that are not germane to conveying an understanding of theinventive subject matter have been omitted from the figures. However, askilled artisan will readily recognize that various additionalfunctional components may be included as part of the shared dataprocessing platform 100 to facilitate additional functionality that isnot specifically described herein.

As shown, the shared data processing platform 100 comprises thenetwork-based database system 102, a cloud computing storage platform104 (e.g., a storage platform, an AWS® service, Microsoft Azure®, orGoogle Cloud Services®), and a remote computing device 106. Thenetwork-based database system 102 is a network-based system used forstoring and accessing data (e.g., internally storing data, accessingexternal remotely located data) in an integrated manner, and reportingand analysis of the integrated data from the one or more disparatesources (e.g., the cloud computing storage platform 104). The cloudcomputing storage platform 104 comprises a plurality of computingmachines and provides on-demand computer system resources such as datastorage and computing power to the network-based database system 102.While in the embodiment illustrated in FIG. 1 , a network-based databasesystem is depicted, other embodiments may include other types ofdatabases or other data processing systems.

The remote computing device 106 (e.g., a user device such as a laptopcomputer) comprises one or more computing machines (e.g., a user devicesuch as a laptop computer) that execute a remote software component 108(e.g., browser accessed cloud service) to provide additionalfunctionality to users of the network-based database system 102. Theremote software component 108 comprises a set of machine-readableinstructions (e.g., code) that, when executed by the remote computingdevice 106, cause the remote computing device 106 to provide certainfunctionality. The remote software component 108 may operate on inputdata and generates result data based on processing, analyzing, orotherwise transforming the input data. As an example, the remotesoftware component 108 can be a data provider or data consumer thatenables database tracking procedures, such as streams on shared tablesand views, as discussed in further detail below.

The network-based database system 102 comprises an access managementsystem 110, a compute service manager 112, an execution platform 114,and a database 116. The access management system 110 enablesadministrative users to manage access to resources and services providedby the network-based database system 102. Administrative users cancreate and manage users, roles, and groups, and use permissions to allowor deny access to resources and services. The access management system110 can store share data that securely manages shared access to thestorage resources of the cloud computing storage platform 104 amongstdifferent users of the network-based database system 102, as discussedin further detail below.

The compute service manager 112 coordinates and manages operations ofthe network-based database system 102. The compute service manager 112also performs query optimization and compilation as well as managingclusters of computing services that provide compute resources (e.g.,virtual warehouses, virtual machines, EC2 clusters). The compute servicemanager 112 can support any number of client accounts such as end usersproviding data storage and retrieval requests, system administratorsmanaging the systems and methods described herein, and othercomponents/devices that interact with compute service manager 112.

The compute service manager 112 is also coupled to database 116, whichis associated with the entirety of data stored on the shared dataprocessing platform 100. The database 116 stores data pertaining tovarious functions and aspects associated with the network-based databasesystem 102 and its users.

In some embodiments, database 116 includes a summary of data stored inremote data storage systems as well as data available from one or morelocal caches. Additionally, database 116 may include informationregarding how data is organized in the remote data storage systems andthe local caches. Database 116 allows systems and services to determinewhether a piece of data needs to be accessed without loading oraccessing the actual data from a storage device. The compute servicemanager 112 is further coupled to an execution platform 114, whichprovides multiple computing resources (e.g., virtual warehouses) thatexecute various data storage and data retrieval tasks, as discussed ingreater detail below.

Execution platform 114 is coupled to multiple data storage devices 124-1to 124-n that are part of a cloud computing storage platform 104. Insome embodiments, data storage devices 124-1 to 124-n are cloud-basedstorage devices located in one or more geographic locations. Forexample, data storage devices 124-1 to 124-n may be part of a publiccloud infrastructure or a private cloud infrastructure. Data storagedevices 124-1 to 124-n may be hard disk drives (HDDs), solid statedrives (SSDs), storage clusters, Amazon S3 storage systems or any otherdata storage technology. Additionally, cloud computing storage platform104 may include distributed file systems (such as Hadoop DistributedFile Systems (HDFS)), object storage systems, and the like.

The execution platform 114 comprises a plurality of compute nodes (e.g.,virtual warehouses). A set of processes on a compute node executes aquery plan compiled by the compute service manager 112. The set ofprocesses can include: a first process to execute the query plan; asecond process to monitor and delete micro-partition files using a leastrecently used (LRU) policy, and implement an out of memory (OOM) errormitigation process; a third process that extracts health informationfrom process logs and status information to send back to the computeservice manager 112; a fourth process to establish communication withthe compute service manager 112 after a system boot; and a fifth processto handle all communication with a compute cluster for a given jobprovided by the compute service manager 112 and to communicateinformation back to the compute service manager 112 and other computenodes of the execution platform 114.

The cloud computing storage platform 104 also comprises an accessmanagement system 118 and a web proxy 120. As with the access managementsystem 110, the access management system 118 allows users to create andmanage users, roles, and groups, and use permissions to allow or denyaccess to cloud services and resources. The access management system 110of the network-based database system 102 and the access managementsystem 118 of the cloud computing storage platform 104 can communicateand share information so as to enable access and management of resourcesand services shared by users of both the network-based database system102 and the cloud computing storage platform 104. The web proxy 120handles tasks involved in accepting and processing concurrent API calls,including traffic management, authorization and access control,monitoring, and API version management. The web proxy 120 provides HTTPproxy service for creating, publishing, maintaining, securing, andmonitoring APIs (e.g., REST APIs).

In some embodiments, communication links between elements of the shareddata processing platform 100 are implemented via one or more datacommunication networks. These data communication networks may utilizeany communication protocol and any type of communication medium. In someembodiments, the data communication networks are a combination of two ormore data communication networks (or sub-networks) coupled to oneanother. In alternate embodiments, these communication links areimplemented using any type of communication medium and any communicationprotocol.

As shown in FIG. 1 , data storage devices 124-1 to 124-N are decoupledfrom the computing resources associated with the execution platform 114.That is, new virtual warehouses can be created and terminated in theexecution platform 114 and additional data storage devices can becreated and terminated on the cloud computing storage platform 104 in anindependent manner. This architecture supports dynamic changes to thenetwork-based database system 102 based on the changing datastorage/retrieval needs as well as the changing needs of the users andsystems accessing the shared data processing platform 100. The supportof dynamic changes allows network-based database system 102 to scalequickly in response to changing demands on the systems and componentswithin network-based database system 102. The decoupling of thecomputing resources from the data storage devices 124-1 to 124-nsupports the storage of large amounts of data without requiring acorresponding large amount of computing resources. Similarly, thisdecoupling of resources supports a significant increase in the computingresources utilized at a particular time without requiring acorresponding increase in the available data storage resources.Additionally, the decoupling of resources enables different accounts tohandle creating additional compute resources to process data shared byother users without affecting the other users' systems. For instance, adata provider may have three compute resources and share data with adata consumer, and the data consumer may generate new compute resourcesto execute queries against the shared data, where the new computeresources are managed by the data consumer and do not affect or interactwith the compute resources of the data provider.

Compute service manager 112, database 116, execution platform 114, cloudcomputing storage platform 104, and remote computing device 106 areshown in FIG. 1 as individual components. However, each of computeservice manager 112, database 116, execution platform 114, cloudcomputing storage platform 104, and remote computing environment may beimplemented as a distributed system (e.g., distributed across multiplesystems/platforms at multiple geographic locations) connected by APIsand access information (e.g., tokens, login data). Additionally, each ofcompute service manager 112, database 116, execution platform 114, andcloud computing storage platform 104 can be scaled up or down(independently of one another) depending on changes to the requestsreceived and the changing needs of shared data processing platform 100.Thus, in the described embodiments, the network-based database system102 is dynamic and supports regular changes to meet the current dataprocessing needs.

During typical operation, the network-based database system 102processes multiple jobs (e.g., queries) determined by the computeservice manager 112. These jobs are scheduled and managed by the computeservice manager 112 to determine when and how to execute the job. Forexample, the compute service manager 112 may divide the job intomultiple discrete tasks and may determine what data is needed to executeeach of the multiple discrete tasks. The compute service manager 112 mayassign each of the multiple discrete tasks to one or more nodes of theexecution platform 114 to process the task. The compute service manager112 may determine what data is needed to process a task and furtherdetermine which nodes within the execution platform 114 are best suitedto process the task. Some nodes may have already cached the data neededto process the task (due to the nodes having recently downloaded thedata from the cloud computing storage platform 104 for a previous job)and, therefore, be a good candidate for processing the task. Metadatastored in the database 116 assists the compute service manager 112 indetermining which nodes in the execution platform 114 have alreadycached at least a portion of the data needed to process the task. One ormore nodes in the execution platform 114 process the task using datacached by the nodes and, if necessary, data retrieved from the cloudcomputing storage platform 104. It is desirable to retrieve as much dataas possible from caches within the execution platform 114 because theretrieval speed is typically much faster than retrieving data from thecloud computing storage platform 104.

As shown in FIG. 1 , the shared data processing platform 100 separatesthe execution platform 114 from the cloud computing storage platform104. In this arrangement, the processing resources and cache resourcesin the execution platform 114 operate independently of the data storagedevices 124-1 to 124-n in the cloud computing storage platform 104.Thus, the computing resources and cache resources are not restricted tospecific data storage devices 124-1 to 124-n. Instead, all computingresources and all cache resources may retrieve data from, and store datato, any of the data storage resources in the cloud computing storageplatform 104.

FIG. 2 is a block diagram illustrating components of the compute servicemanager 112, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 2 , a request processing service 202manages received data storage requests and data retrieval requests(e.g., jobs to be performed on database data). For example, the requestprocessing service 202 may determine the data necessary to process areceived query (e.g., a data storage request or data retrieval request).The data may be stored in a cache within the execution platform 114 orin a data storage device in cloud computing storage platform 104. Amanagement console service 204 supports access to various systems andprocesses by administrators and other system managers. Additionally, themanagement console service 204 may receive a request to execute a joband monitor the workload on the system. The stream share engine 225manages change tracking on database objects, such as a data share (e.g.,shared table) or shared view, according to some example embodiments, andas discussed in further detail below.

The compute service manager 112 also includes a job compiler 206, a joboptimizer 208, and a job executor 210. The job compiler 206 parses a jobinto multiple discrete tasks and generates the execution code for eachof the multiple discrete tasks. The job optimizer 208 determines thebest method to execute the multiple discrete tasks based on the datathat needs to be processed. The job optimizer 208 also handles variousdata pruning operations and other data optimization techniques toimprove the speed and efficiency of executing the job. The job executor210 executes the execution code for jobs received from a queue ordetermined by the compute service manager 112.

A job scheduler and coordinator 212 sends received jobs to theappropriate services or systems for compilation, optimization, anddispatch to the execution platform 114. For example, jobs may beprioritized and processed in that prioritized order. In an embodiment,the job scheduler and coordinator 212 determines a priority for internaljobs that are scheduled by the compute service manager 112 with other“outside” jobs such as user queries that may be scheduled by othersystems in the database but may utilize the same processing resources inthe execution platform 114. In some embodiments, the job scheduler andcoordinator 212 identifies or assigns particular nodes in the executionplatform 114 to process particular tasks. A virtual warehouse manager214 manages the operation of multiple virtual warehouses implemented inthe execution platform 114. As discussed below, each virtual warehouseincludes multiple execution nodes that each include a cache and aprocessor (e.g., a virtual machine, an operating system level containerexecution environment).

Additionally, the compute service manager 112 includes a configurationand metadata manager 216, which manages the information related to thedata stored in the remote data storage devices and in the local caches(i.e., the caches in execution platform 114). The configuration andmetadata manager 216 uses the metadata to determine which datamicro-partitions need to be accessed to retrieve data for processing aparticular task or job. A monitor and workload analyzer 218 overseesprocesses performed by the compute service manager 112 and manages thedistribution of tasks (e.g., workload) across the virtual warehouses andexecution nodes in the execution platform 114. The monitor and workloadanalyzer 218 also redistributes tasks, as needed, based on changingworkloads throughout the network-based database system 102 and mayfurther redistribute tasks based on a user (e.g., “external”) queryworkload that may also be processed by the execution platform 114. Theconfiguration and metadata manager 216 and the monitor and workloadanalyzer 218 are coupled to a data storage device 220. Data storagedevice 220 in FIG. 2 represent any data storage device within thenetwork-based database system 102. For example, data storage device 220may represent caches in execution platform 114, storage devices in cloudcomputing storage platform 104, or any other storage device.

FIG. 3 is a block diagram illustrating components of the executionplatform 114, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 3 , execution platform 114 includesmultiple virtual warehouses, which are elastic clusters of computeinstances, such as virtual machines. In the example illustrated, thevirtual warehouses include virtual warehouse 1, virtual warehouse 2, andvirtual warehouse n. Each virtual warehouse (e.g., EC2 cluster) includesmultiple execution nodes (e.g., virtual machines) that each include adata cache and a processor. The virtual warehouses can execute multipletasks in parallel by using the multiple execution nodes. As discussedherein, execution platform 114 can add new virtual warehouses and dropexisting virtual warehouses in real time based on the current processingneeds of the systems and users. This flexibility allows the executionplatform 114 to quickly deploy large amounts of computing resources whenneeded without being forced to continue paying for those computingresources when they are no longer needed. All virtual warehouses canaccess data from any data storage device (e.g., any storage device incloud computing storage platform 104).

Although each virtual warehouse shown in FIG. 3 includes three executionnodes, a particular virtual warehouse may include any number ofexecution nodes. Further, the number of execution nodes in a virtualwarehouse is dynamic, such that new execution nodes are created whenadditional demand is present, and existing execution nodes are deletedwhen they are no longer necessary (e.g., upon a query or jobcompletion).

Each virtual warehouse is capable of accessing any of the data storagedevices 124-1 to 124-n shown in FIG. 1 . Thus, the virtual warehousesare not necessarily assigned to a specific data storage device 124-1 to124-n and, instead, can access data from any of the data storage devices124-1 to 124-n within the cloud computing storage platform 104.Similarly, each of the execution nodes shown in FIG. 3 can access datafrom any of the data storage devices 124-1 to 124-n. For instance, thestorage device 124-1 of a first user (e.g., provider account user) maybe shared with a worker node in a virtual warehouse of another user(e.g., consumer account user), such that the other user can create adatabase (e.g., read-only database) and use the data in storage device124-1 directly without needing to copy the data (e.g., copy it to a newdisk managed by the consumer account user). In some embodiments, aparticular virtual warehouse or a particular execution node may betemporarily assigned to a specific data storage device, but the virtualwarehouse or execution node may later access data from any other datastorage device.

In the example of FIG. 3 , virtual warehouse 1 includes three executionnodes 302-1, 302-2, and 302-n. Execution node 302-1 includes a cache304-1 and a processor 306-1. Execution node 302-2 includes a cache 304-2and a processor 306-2. Execution node 302-n includes a cache 304-n and aprocessor 306-n. Each execution node 302-1, 302-2, and 302-n isassociated with processing one or more data storage and/or dataretrieval tasks. For example, a virtual warehouse may handle datastorage and data retrieval tasks associated with an internal service,such as a clustering service, a materialized view refresh service, afile compaction service, a storage procedure service, or a file upgradeservice. In other implementations, a particular virtual warehouse mayhandle data storage and data retrieval tasks associated with aparticular data storage system or a particular category of data.

Similar to virtual warehouse 1 discussed above, virtual warehouse 2includes three execution nodes 312-1, 312-2, and 312-n. Execution node312-1 includes a cache 314-1 and a processor 316-1. Execution node 312-2includes a cache 314-2 and a processor 316-2. Execution node 312-nincludes a cache 314-n and a processor 316-n. Additionally, virtualwarehouse 3 includes three execution nodes 322-1, 322-2, and 322-n.Execution node 322-1 includes a cache 324-1 and a processor 326-1.Execution node 322-2 includes a cache 324-2 and a processor 326-2.Execution node 322-n includes a cache 324-n and a processor 326-n.

In some embodiments, the execution nodes shown in FIG. 3 are statelesswith respect to the data the execution nodes are caching. For example,these execution nodes do not store or otherwise maintain stateinformation about the execution node, or the data being cached by aparticular execution node. Thus, in the event of an execution nodefailure, the failed node can be transparently replaced by another node.Since there is no state information associated with the failed executionnode, the new (replacement) execution node can easily replace the failednode without concern for recreating a particular state.

Although the execution nodes shown in FIG. 3 each include one data cacheand one processor, alternative embodiments may include execution nodescontaining any number of processors and any number of caches.Additionally, the caches may vary in size among the different executionnodes. The caches shown in FIG. 3 store, in the local execution node(e.g., local disk), data that was retrieved from one or more datastorage devices in cloud computing storage platform 104 (e.g., S3objects recently accessed by the given node). In some exampleembodiments, the cache stores file headers and individual columns offiles as a query downloads only columns necessary for that query.

To improve cache hits and avoid overlapping redundant data stored in thenode caches, the job optimizer 208 assigns input file sets to the nodesusing a consistent hashing scheme to hash over table file names of thedata accessed (e.g., data in database 116 or database 122). Subsequentor concurrent queries accessing the same table file will therefore beperformed on the same node, according to some example embodiments.

As discussed, the nodes and virtual warehouses may change dynamically inresponse to environmental conditions (e.g., disaster scenarios),hardware/software issues (e.g., malfunctions), or administrative changes(e.g., changing from a large cluster to smaller cluster to lower costs).In some example embodiments, when the set of nodes changes, no data isreshuffled immediately. Instead, the least recently used replacementpolicy is implemented to eventually replace the lost cache contents overmultiple jobs. Thus, the caches reduce or eliminate the bottleneckproblems occurring in platforms that consistently retrieve data fromremote storage systems. Instead of repeatedly accessing data from theremote storage devices, the systems and methods described herein accessdata from the caches in the execution nodes, which is significantlyfaster and avoids the bottleneck problem discussed above. In someembodiments, the caches are implemented using high-speed memory devicesthat provide fast access to the cached data. Each cache can store datafrom any of the storage devices in the cloud computing storage platform104.

Further, the cache resources and computing resources may vary betweendifferent execution nodes. For example, one execution node may containsignificant computing resources and minimal cache resources, making theexecution node useful for tasks that require significant computingresources. Another execution node may contain significant cacheresources and minimal computing resources, making this execution nodeuseful for tasks that require caching of large amounts of data. Yetanother execution node may contain cache resources providing fasterinput-output operations, useful for tasks that require fast scanning oflarge amounts of data. In some embodiments, the execution platform 114implements skew handling to distribute work amongst the cache resourcesand computing resources associated with a particular execution, wherethe distribution may be further based on the expected tasks to beperformed by the execution nodes. For example, an execution node may beassigned more processing resources if the tasks performed by theexecution node become more processor-intensive. Similarly, an executionnode may be assigned more cache resources if the tasks performed by theexecution node require a larger cache capacity. Further, some nodes maybe executing much slower than others due to various issues (e.g.,virtualization issues, network overhead). In some example embodiments,the imbalances are addressed at the scan level using a file stealingscheme. In particular, whenever a node process completes scanning itsset of input files, it requests additional files from other nodes. Ifthe one of the other nodes receives such a request, the node analyzesits own set (e.g., how many files are left in the input file set whenthe request is received), and then transfers ownership of one or more ofthe remaining files for the duration of the current job (e.g., query).The requesting node (e.g., the file stealing node) then receives thedata (e.g., header data) and downloads the files from the cloudcomputing storage platform 104 (e.g., from data storage device 124-1),and does not download the files from the transferring node. In this way,lagging nodes can transfer files via file stealing in a way that doesnot worsen the load on the lagging nodes.

Although virtual warehouses 1, 2, and n are associated with the sameexecution platform 114, the virtual warehouses may be implemented usingmultiple computing systems at multiple geographic locations. Forexample, virtual warehouse 1 can be implemented by a computing system ata first geographic location, while virtual warehouses 2 and n areimplemented by another computing system at a second geographic location.In some embodiments, these different computing systems are cloud-basedcomputing systems maintained by one or more different entities.

Additionally, each virtual warehouse is shown in FIG. 3 as havingmultiple execution nodes. The multiple execution nodes associated witheach virtual warehouse may be implemented using multiple computingsystems at multiple geographic locations. For example, an instance ofvirtual warehouse 1 implements execution nodes 302-1 and 302-2 on onecomputing platform at a geographic location and implements executionnode 302-n at a different computing platform at another geographiclocation. Selecting particular computing systems to implement anexecution node may depend on various factors, such as the level ofresources needed for a particular execution node (e.g., processingresource requirements and cache requirements), the resources availableat particular computing systems, communication capabilities of networkswithin a geographic location or between geographic locations, and whichcomputing systems are already implementing other execution nodes in thevirtual warehouse.

Execution platform 114 is also fault tolerant. For example, if onevirtual warehouse fails, that virtual warehouse is quickly replaced witha different virtual warehouse at a different geographic location.

A particular execution platform 114 may include any number of virtualwarehouses. Additionally, the number of virtual warehouses in aparticular execution platform is dynamic, such that new virtualwarehouses are created when additional processing and/or cachingresources are needed. Similarly, existing virtual warehouses may bedeleted when the resources associated with the virtual warehouse are nolonger necessary.

In some embodiments, the virtual warehouses may operate on the same datain cloud computing storage platform 104, but each virtual warehouse hasits own execution nodes with independent processing and cachingresources. This configuration allows requests on different virtualwarehouses to be processed independently and with no interferencebetween the requests. This independent processing, combined with theability to dynamically add and remove virtual warehouses, supports theaddition of new processing capacity for new users without impacting theperformance observed by the existing users.

FIG. 4 shows an example of two separate accounts in a data warehousesystem, according to some example embodiments. Here, Company A mayoperate an account A 402 with a network-based data warehouse system asdescribed herein. In account A 402, Company A data 404 may be stored.The Company A data 404 may include, for example, customer data 406relating to customers of Company A. The customer data 406 may be storedin a table or other format storing customer information and otherrelated information. The other related information may includeidentifying information, such as email, and other known characteristicsof the customers, such as gender, geographic location, purchasinghabits, and the like. For example, if Company A is a consumer-goodscompany, purchasing characteristics may be stored, such as whether thecustomer is single, married, part of a suburban or urban family, etc. IfCompany A is a streaming service company, information about the watchinghabits of customers may be stored, such as whether the customer likessci-fi, nature, reality, action, etc.

Likewise, Company B may operate an account B 412 with the network-baseddatabase system as described herein. In account B 412, Company B data414 may be stored. The Company B data 414 may include, for example,customer data relating to customers of Company B. The customer data 416may be stored in a table or other format storing customer informationand other related information. The other related information may includeidentifying information, such as email, and other known characteristicsof the customers, such as gender, geographic location, purchasinghabits, etc., as described above.

For security and privacy reasons, Company A's data may not be accessibleto Company B and vice versa. However, Company A and Company B may wantto share at least some of their data with each other without revealingsensitive information, such as a customer's personal identityinformation. For example, Company A and Company B may want to explorecross marketing or advertising opportunities and may want to see howmany of their customers overlap and filter based on certaincharacteristics of the overlapping customers to identify relationshipsand patterns.

To this end, a data clean room may be provided by the network-baseddatabase system as described herein. FIG. 5 is a block diagramillustrating a method for operating a data clean room, according to someexample embodiments. The data clean room may enable companies A and B toperform overlap analysis on their company data, without sharingsensitive data and without losing control over the data. The data cleanroom may create linkages between the data for each account and mayinclude a set of blind cross reference tables.

Next, example operations to create the data clean room are described.Account B may include customer data 502 for Company B, and account A mayinclude customer data 504 for Company A. In this example, account B mayinitiate the creation of the data clean room; however, either accountmay initiate creation of the data clean room. Account B may create asecure function 506. The secure function 506 may look up specificidentifier information in account B's customer data 502. The securefunction 506 may anonymize the information by creating identifiers foreach customer data (e.g., generating a first result set). The securefunction 506 may be a secure user-defined function (UDF) and may beimplemented using the techniques described in U.S. patent applicationSer. No. 16/814,875, entitled “System and Method for Global DataSharing,” filed on Mar. 10, 2020, which is incorporated herein byreference in its entirety, including but not limited to those portionsthat specifically appear hereinafter, the incorporation by referencebeing made with the following exception: In the event that any portionof the above-referenced application is inconsistent with thisapplication, this application supersedes the above-referencedapplication.

The secure function 506 may be implemented as a SQL UDF. The securefunction 506 may be defined to protect the underlying data used toprocess the function. As such, the secure function 506 may restrictdirect and indirect exposure of the underlying data.

The secure function 506 may then be shared with account A using a secureshare 508. The secure share 508 may allow account A to execute thesecure function 506 while restricting account A from having access tothe underlying data of account B used by the function and from beingable to see the code of the function. The secure share 508 may alsorestrict account A from accessing the code of the secure function 506.Moreover, the secure share 508 may restrict account A from seeing anylogs or other information about account B's use of the secure function506 or the parameters provided by account B of the secure function 506when it is called.

Account A may execute the secure function 506 using its customer data504 (e.g., generating a second result set). The result of the executionof the secure function 506 may be communicated to account B. Forinstance, a cross reference table 510 may be created in account B, whichmay include anonymized customer information 512 (e.g., anonymizedidentification information, private salted by provider's secret salt).Likewise, a cross reference table 514 may be created in account A, whichmay include anonymized customer information 516 for matching overlappingcustomers for both companies, and dummy identifiers for non-matchingrecords. The data from the two companies may be securely joined so thatneither account may access the underlying data or other identifiableinformation. For example, the data may be securely joined using thetechniques described in U.S. patent application Ser. No. 16/368,339,entitled “Secure Data Joins in a Multiple Tenant Database System,” filedon May 28, 2019, which is incorporated herein by reference in itsentirety, including but not limited to those portions that specificallyappear hereinafter, the incorporation by reference being made with thefollowing exception: In the event that any portion of theabove-referenced application is inconsistent with this application, thisapplication supersedes the above-referenced application.

For instance, cross reference table 510 (and anonymized customerinformation 512) may include fields: “my_cust_id,” which may correspondto the customer ID in account B's data; “my_link_id,” which maycorrespond to an anonymized link to the identified customer information;and a “their_link_id,” which may correspond to an anonymized matchedcustomer in company A. “their_link_id” may be anonymized (e.g., salted),so that company B cannot discern the identity of the matched customers.The anonymization may be performed using hashing, encryption,tokenization, or other suitable techniques.

Moreover, to further anonymize the identity, all listed customers ofcompany B in cross reference table 510 (and anonymized customerinformation 512) may have a unique matched customer from company Blisted, irrespective of whether there was an actual match or not. Adummy “their_link_id” may be created for customers not matched. This wayneither company may be able to ascertain identity information of thematched customers. Neither company may discern where there is an actualmatch rather than a dummy returned identifier (no match). Hence, thecross reference tables 510 may include anonymized key-value pairs. Asummary report may be created notifying the total number of matches, butother details of the matched customers may not be provided to safeguardthe identities of the customers.

The data clean room may operate in one or both directions, meaning thata double-blind clean room may be provided. FIG. 6 is a block diagramillustrating a method for operating a double-blind clean room, accordingto some example embodiments. The double-blind clean room may enablecompany A to perform overlap analysis using its company data with thecompany data of Company B and vice versa, without sharing sensitive dataand without losing control over their own data. The double-blind cleanroom may create linkages between the data for each account and mayinclude a set of double-blind cross reference tables.

Here, account A may include its customer data 602, and account B mayinclude its customer data 604. Account A may create a secure function606 (“Get_Link_ID”), as described above. The secure function 606 may beshared with account B using a secure share 608, as described above.Moreover, a stored-procedures function 610 may detect changes to data inrespective customer data and may update and refresh links accordingly.

The same or similar process may be applied from account B to account Awith secure function 612, secure share 614, and stored procedures 616.Consequently, the cross reference table 618 in account A may includeinformation about the customer overlap between the two companies. Forexample, the cross reference table 618 includes fields: “my_cust_id,”which may correspond to the customer ID in account A's data;“my_link_id,” which may correspond to an anonymized link to theidentified customer information of company A; and a “their_link_id,”which may correspond to an anonymized matched customer in company B. Theanonymization may be performed using hashing, encryption, tokenization,or the like.

Similarly, the cross reference table 620 in account B may includeinformation about the customer overlap between the two companies. Forexample, the cross reference table 620 includes fields: “my_cust_id,”which may correspond to the customer ID in account B's data;“my_link_id,” which may correspond to an anonymized link to theidentified customer information of company B; and a “their_link_id,”which may correspond to an anonymized matched customer in company A. Theanonymization may be performed using hashing, encryption, tokenization,or other suitable techniques.

FIG. 7A-7C show an example architecture for implementing data cleanrooms across different regions, according to some example embodiments.In particular, FIG. 7A shows a client account 700 that is in a firstregion (region 1), FIG. 7B shows a provider replica account 740 in thefirst region (region 1), and FIG. 7C shows a provider account 760 thatis in a second region (region 2). Data is exchanged between thedifferent accounts as indicated by the discontinued arrows linking thedifferent figures (arrows A, B, C, and D). In accordance with someexample embodiments, the data exchanged between the architecture FIG. 7Aand FIG. 7B is performed via data shares (e.g., repointing of metadatawithin the same cloud) as indicated by the double-headed “shares” arrowat the top of FIGS. 7A and 7B. Further, in accordance with some exampleembodiments, the data exchanged between the architecture of FIG. 7B andFIG. 7C is performed via data replication (e.g., actual moving of data),as indicated by the double-headed “replication” arrow at the top ofFIGS. 7B and 7C.

With reference to FIG. 7C, the provider account 760 receives a requestfrom the client account 700 to perform multi-cloud and/or multi-regionclean room queries, where the provider account 760 is in region 2 (e.g.,New York City hosted virtual cloud), and the client account 700 is inregion 1 (e.g., San Francisco hosted virtual cloud). In response to therequest to perform queries using a clean room, the provider account 760creates a provider replica account 740 (FIG. 7B) in the same distributeddatabase network (e.g., region and/or provider) as the client account700 (e.g., region 1, as in FIG. 7A).

In some example embodiments, to initiate the virtual cloud data cleanroom, a clean room cross-reference table 742A shown in FIG. 7C (e.g.,clean room 510) is generated based on the provider data set 746 (FIG.7C), as discussed above with reference to FIGS. 4-6 . The clean roomcross-reference table 742A comprises a provider account link ID, whichis privately hashed using a provider account private salt, and thencommonly hashed using the common salt (a salt previously agreed to byclient account 700 and provider account 760, as a cross reference key),which creates a common link between the two data sets: a provider dataset 746 (FIG. 7C) and requester data set 702 of FIG. 7A (e.g., a clientdataset). Further, the secure function 744A of FIG. 7C (e.g., securefunction 506) is replicated to the provider replica account 740 assecure function 744B, and then shared to the client account 700 for useby the client as a secure function 744C (e.g., a secure sharedfunction).

With reference to FIG. 7A, the client account 700 initiates a storedprocedure 704 to generate the client's portion of the clean roomcross-reference table 742C (e.g., clean room cross reference table 514).In calling the secure function 744C, the stored procedure 704 passesinto the secure function 744C the agreed-upon ID (e.g., ID for users tocross reference in the table, such as email addresses of the users inthe respective client and provider datasets). The secure function 744Clooks up the identifier (e.g., a given email) using the hashing functionand the common salt to determine whether a given identifier exists inthe provider's clean room cross-reference table 742A (FIG. 7C). If thelooked-up identifier has a match in the provider data, the securefunction 744C (which is a shared function, as discussed above) providesthe provider's identifier (e.g., the email in privately salted form bythe private salt of the provider) for inclusion in the clean roomcross-reference table 742C (FIG. 7A) of the client account 700.Alternatively, if the looked-up identifier does not have a match in theprovider data, a dummy value is returned, as discussed above withreference to FIGS. 4-6 . For example, the secure function 744C receivesthe identifier that is passed in by the call (of the stored procedure704) but then hashes it with a different private salt (random value) tocreate a dummy value for inclusion in the clean room cross-referencetable 742C (FIG. 7A), in accordance with some example embodiments.

Once the cross-reference table matching has been completed, the clientaccount 700 may generate one or more query requests to query data thatis in the data clean room shared between the client and provider. Incontrast to the approaches of FIGS. 4-6 , not all of the provider dataset 746 is used in the clean room, as the data set may be too large forreplication (to the provider replica data account 740), or replicationmay be impractical or too costly. To address the issue, the clientaccount 700 in a clean room shared 708A (FIG. 7A) includes a my datatable 710A which includes only the data (e.g., columns) that are relatedto a given query requested by the client account 700. Further, the mysecure query requests table 712A in the clean room shared 708A storesthe actual queries for tracking and processing. The clean room shared708A is shared to the provider replica account 740 as clean room share708B, which includes the my data table 710B and my secure query requeststable 712B as share objects (via metadata pointing based access controlwithin the first cloud of client account 700 and provider replicaaccount 740, e.g., in region 1).

In some example embodiments, shared tables are not allowed to bereplicated within the environment of the networked-based database system102 to limit access to shared data and increase security and privacy. Tothis end, the provider replica account 740 implements a clean roomrequests database 745A that includes streams and tasks to stream thedata of the clean room share 708B and generate new tables (e.g., interimtable(s)), including a my secure query requests table 748A and my datatable 750A within the provider replica account 740.

Further, the my secure query requests table 748A and my data table 750Aare no longer shares, such that the tables are then replicated to theclean room 745B (FIG. 7C) as my data table 750B and my secure queryrequests table 748B on the provider account 760. In some exampleembodiments, the streams and tasks in the clean room requests database745 call stored procedure load request 749 when a new request arrives inmy secure query requests table 712B, which is used to determine whetherthe my data table 750A needs to be re-created to keep the data up todate (e.g., check whether there is new client data). In some exampleembodiments, if the stored procedure in the clean room request database745A determines that the data is up to date, then the new my data table750A does not need to be recreated and the table that was generatedpreviously that is stored on the provider account 760 (previously storedtable 750B) is utilized to reduce computation and unnecessaryreplication of data. Alternatively, if the stored procedure load request749 determines that the data is not up to date, then the storedprocedure will regenerate the my data table 750A, which is thenreplicated over to provider account 760. In both cases, the table mysecure query requests 748A is replicated to initiate the request onaccount 760.

To perform query processing, the provider account 760 then initiates atask 771 that calls the stored procedure 772 to complete the clientqueries using the clean room cross-reference table 742A and generate theresults table 780A. The results table 780A is then replicated to theprovider replica account 740 as a results table 780B and then shared tothe client account 700 as the results table 780C. In some exampleembodiments, for each query in the my secure query requests table 748Bthat is to be processed, a task 785 is initiated by the provider account760 to determine whether the my data table 750B needs to be refreshedwith new client data. That is, for example, the tasks 777 (FIG. 7B) iscalled to determine whether there is new data for the tables when theclient is sending the my data table and queries to the provider (e.g.,task 777 can refresh the replica account databases, including theresults tables 780B and cross reference table 742B, from the provideraccount 760); whereas the tasks 785 is called when the queries are run(by the provider account 760) to determine whether the provider account760 should first update its dataset before processing the queries.

Although the illustrated examples of FIGS. 7A-7C discuss implementing aclean room across different regions (region 1 and region 2), thearchitecture of FIG. 7A-7C can also be implemented for the same regionacross different cloud providers. For example, the client account 700and the provider replica account 740 can be Microsoft Azure clouds inthe same region (region 1), whereas the provider account 760 can be froma different provider, such as an Amazon AWS cloud that is in same region(region 1). Additionally, the architecture of FIG. 7A-7C can also beimplemented for combinations of different regions and cloud providers.For example, the client account 700 and provider replica account 740 canbe Microsoft Azure clouds in region 1; whereas the provider account 760can be from a different cloud provider, such as an Amazon AWS cloud, andfurther be located in a different region (region 2). In this way,different user accounts of the network-based database system 102 canefficiently and securely implement the data clean room across differentcloud provider networks and different regions.

FIG. 8 shows a flow diagram of a method 800 for implementing a dataclean room on different regions and clouds, according to some exampleembodiments. At operation 802, the provider account 760 generates aprovider replica account 740. At operation 804, the cross-referencetables are generated and shared across the accounts (e.g., clean roomcross-reference tables 742A-C). At operation 806, one or more clientquery requests are received with client data to be processed in thequeries (e.g., my data table 710A, my secure query requests table 712A).At operation 808, the provider replica account 740 and provider account760 process the client query requests to generate the results table 780A(e.g., via generating and replication of the my secure query requeststable 748A and the my data table 750A as discussed above). At operation810, the results data is transmitted from the provider account 760 tothe client account 700 (e.g., via replication and sharing using theprovider replica account 740).

FIG. 9 illustrates a diagrammatic representation of a machine 900 in theform of a computer system within which a set of instructions may beexecuted for causing the machine 900 to perform any one or more of themethodologies discussed herein, according to an example embodiment.Specifically, FIG. 9 shows a diagrammatic representation of the machine900 in the example form of a computer system, within which instructions916 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 900 to perform any one ormore of the methodologies discussed herein may be executed. For example,the instructions 916 may cause the machine 900 to execute any one ormore operations of any one or more of the methods described herein. Asanother example, the instructions 916 may cause the machine 900 toimplemented portions of the data flows described herein. In this way,the instructions 916 transform a general, non-programmed machine into aparticular machine 900 (e.g., the remote computing device 106, theaccess management system 110, the compute service manager 112, theexecution platform 114, the access management system 118, the Web proxy120, remote computing device 106) that is specially configured to carryout any one of the described and illustrated functions in the mannerdescribed herein.

In alternative embodiments, the machine 900 operates as a standalonedevice or may be coupled (e.g., networked) to other machines. In anetworked deployment, the machine 900 may operate in the capacity of aserver machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 900 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a smart phone, a mobiledevice, a network router, a network switch, a network bridge, or anymachine capable of executing the instructions 916, sequentially orotherwise, that specify actions to be taken by the machine 900. Further,while only a single machine 900 is illustrated, the term “machine” shallalso be taken to include a collection of machines 900 that individuallyor jointly execute the instructions 916 to perform any one or more ofthe methodologies discussed herein.

The machine 900 includes processors 910, memory 930, and input/output(I/O) components 950 configured to communicate with each other such asvia a bus 902. In an example embodiment, the processors 910 (e.g., acentral processing unit (CPU), a reduced instruction set computing(RISC) processor, a complex instruction set computing (CISC) processor,a graphics processing unit (GPU), a digital signal processor (DSP), anapplication-specific integrated circuit (ASIC), a radio-frequencyintegrated circuit (RFIC), another processor, or any suitablecombination thereof) may include, for example, a processor 912 and aprocessor 914 that may execute the instructions 916. The term“processor” is intended to include multi-core processors 910 that maycomprise two or more independent processors (sometimes referred to as“cores”) that may execute instructions 916 contemporaneously. AlthoughFIG. 9 shows multiple processors 910, the machine 900 may include asingle processor with a single core, a single processor with multiplecores (e.g., a multi-core processor), multiple processors with a singlecore, multiple processors with multiple cores, or any combinationthereof.

The memory 930 may include a main memory 932, a static memory 934, and astorage unit 936, all accessible to the processors 910 such as via thebus 902. The main memory 932, the static memory 934, and the storageunit 936 comprising a machine storage medium 938 may store theinstructions 916 embodying any one or more of the methodologies orfunctions described herein. The instructions 916 may also reside,completely or partially, within the main memory 932, within the staticmemory 934, within the storage unit 936, within at least one of theprocessors 910 (e.g., within the processor's cache memory), or anysuitable combination thereof, during execution thereof by the machine900.

The I/O components 950 include components to receive input, provideoutput, produce output, transmit information, exchange information,capture measurements, and so on. The specific I/O components 950 thatare included in a particular machine 900 will depend on the type ofmachine. For example, portable machines such as mobile phones willlikely include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 950 mayinclude many other components that are not shown in FIG. 9 . The I/Ocomponents 950 are grouped according to functionality merely forsimplifying the following discussion and the grouping is in no waylimiting. In various example embodiments, the I/O components 950 mayinclude output components 952 and input components 954. The outputcomponents 952 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), other signal generators, and soforth. The input components 954 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or another pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 950 may include communication components 964 operableto couple the machine 900 to a network 980 via a coupler 982 or todevices 960 via a coupling 962. For example, the communicationcomponents 964 may include a network interface component or anothersuitable device to interface with the network 980. In further examples,the communication components 964 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, and other communication components to provide communicationvia other modalities. The devices 960 may be another machine or any of awide variety of peripheral devices (e.g., a peripheral device coupledvia a universal serial bus (USB)). For example, as noted above, themachine 900 may correspond to any one of the remote computing device106, the access management system 110, the compute service manager 112,the execution platform 114, the access management system 118, the Webproxy 120, and the devices 960 may include any other of these systemsand devices.

The various memories (e.g., 930, 932, 934, and/or memory of theprocessor(s) 910 and/or the storage unit 936) may store one or more setsof instructions 916 and data structures (e.g., software) embodying orutilized by any one or more of the methodologies or functions describedherein. These instructions 916, when executed by the processor(s) 910,cause various operations to implement the disclosed embodiments.

As used herein, the terms “machine-storage medium,” “device-storagemedium,” and “computer-storage medium” mean the same thing and may beused interchangeably in this disclosure. The terms refer to a single ormultiple storage devices and/or media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storeexecutable instructions and/or data. The terms shall accordingly betaken to include, but not be limited to, solid-state memories, andoptical and magnetic media, including memory internal or external toprocessors. Specific examples of machine-storage media, computer-storagemedia, and/or device-storage media include non-volatile memory,including by way of example semiconductor memory devices, e.g., erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), field-programmable gate arrays(FPGAs), and flash memory devices; magnetic disks such as internal harddisks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks. The terms “machine-storage media,” “computer-storage media,” and“device-storage media” specifically exclude carrier waves, modulateddata signals, and other such media, at least some of which are coveredunder the term “signal medium” discussed below.

In various example embodiments, one or more portions of the network 980may be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local-area network (LAN), a wireless LAN (WLAN), awide-area network (WAN), a wireless WAN (WWAN), a metropolitan-areanetwork (MAN), the Internet, a portion of the Internet, a portion of thepublic switched telephone network (PSTN), a plain old telephone service(POTS) network, a cellular telephone network, a wireless network, aWi-Fi® network, another type of network, or a combination of two or moresuch networks. For example, the network 980 or a portion of the network980 may include a wireless or cellular network, and the coupling 982 maybe a Code Division Multiple Access (CDMA) connection, a Global Systemfor Mobile communications (GSM) connection, or another type of cellularor wireless coupling. In this example, the coupling 982 may implementany of a variety of types of data transfer technology, such as SingleCarrier Radio Transmission Technology (1xRTT), Evolution-Data Optimized(EVDO) technology, General Packet Radio Service (GPRS) technology,Enhanced Data rates for GSM Evolution (EDGE) technology, thirdGeneration Partnership Project (3GPP) including 3G, fourth generationwireless (4G) networks, Universal Mobile Telecommunications System(UMTS), High-Speed Packet Access (HSPA), Worldwide Interoperability forMicrowave Access (WiMAX), Long Term Evolution (LTE) standard, othersdefined by various standard-setting organizations, other long-rangeprotocols, or other data transfer technology.

The instructions 916 may be transmitted or received over the network 980using a transmission medium via a network interface device (e.g., anetwork interface component included in the communication components964) and utilizing any one of a number of well-known transfer protocols(e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions916 may be transmitted or received using a transmission medium via thecoupling 962 (e.g., a peer-to-peer coupling) to the devices 960. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure. The terms “transmissionmedium” and “signal medium” shall be taken to include any intangiblemedium that is capable of storing, encoding, or carrying theinstructions 916 for execution by the machine 900, and include digitalor analog communications signals or other intangible media to facilitatecommunication of such software. Hence, the terms “transmission medium”and “signal medium” shall be taken to include any form of modulated datasignal, carrier wave, and so forth. The term “modulated data signal”means a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in the signal.

The terms “machine-readable medium,” “computer-readable medium,” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms are defined to includeboth machine-storage media and transmission media. Thus, the termsinclude both storage devices/media and carrier waves/modulated datasignals.

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Similarly, the methods described hereinmay be at least partially processor-implemented. For example, at leastsome of the operations of the methods described herein may be performedby one or more processors. The performance of certain of the operationsmay be distributed among the one or more processors, not only residingwithin a single machine, but also deployed across a number of machines.In some example embodiments, the processor or processors may be locatedin a single location (e.g., within a home environment, an officeenvironment, or a server farm), while in other embodiments theprocessors may be distributed across a number of locations.

Although the embodiments of the present disclosure have been describedwith reference to specific example embodiments, it will be evident thatvarious modifications and changes may be made to these embodimentswithout departing from the broader scope of the inventive subjectmatter. Accordingly, the specification and drawings are to be regardedin an illustrative rather than a restrictive sense. The accompanyingdrawings that form a part hereof show, by way of illustration, and notof limitation, specific embodiments in which the subject matter may bepracticed. The embodiments illustrated are described in sufficientdetail to enable those skilled in the art to practice the teachingsdisclosed herein. Other embodiments may be used and derived therefrom,such that structural and logical substitutions and changes may be madewithout departing from the scope of this disclosure. This DetailedDescription, therefore, is not to be taken in a limiting sense, and thescope of various embodiments is defined only by the appended claims,along with the full range of equivalents to which such claims areentitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent, to those of skill inthe art, upon reviewing the above description.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In the appended claims, the terms “including” and“in which” are used as the plain-English equivalents of the respectiveterms “comprising” and “wherein.” Also, in the following claims, theterms “including” and “comprising” are open-ended; that is, a system,device, article, or process that includes elements in addition to thoselisted after such a term in a claim is still deemed to fall within thescope of that claim.

Described implementations of the subject matter can include one or morefeatures, alone or in combination as illustrated below by way ofexample.

Example 1. A method comprising: generating, in a first virtual cloud ofa distributed database, a provider replica database account that is aversion of a provider database account in a second virtual cloud of thedistributed database, the provider database account generated in thefirst virtual cloud based on the first virtual cloud comprising a clientdatabase account; generating a cross reference table that anonymouslycross references client data from a client dataset and provider datafrom a provider dataset, the client dataset being managed by the clientdatabase account on the first virtual cloud, the provider dataset beingmanaged by the provider database account on the second virtual cloud;receiving a query request from the client database account; generating,by the client database account, an interim table by executing a firstportion of the query request using the cross reference table and theclient dataset; transmitting, via the provider replica database accountin the first virtual cloud, the interim table from the client databaseaccount in the first virtual cloud to the provider database account inthe second virtual cloud; receiving, by the client database account, aresults dataset generated by the provider database account according tothe query request, the results dataset generated using the interim tableand the cross reference table to query data from the provider dataset toinclude in the results dataset, the results dataset being received bythe client database account from the provider replica database accountin the first virtual cloud.

Example 2. The method of example 1, wherein the interim table is sharedfrom the client database account to the provider replica databaseaccount in the first virtual cloud, and then replicated from theprovider replica database account to the provider database account inthe second virtual cloud

Example 3. The method of any of examples 1 or 2, wherein the resultdataset is replicated from the provider database account in the secondvirtual cloud to the provider replica database account in the firstvirtual cloud and then shared from the provider replica database accountto the client database account.

Example 4. The method of any of examples 1-3, wherein the interim tablesare regenerated on the provider replica database account, theregenerated interim tables are replicated to the provider databaseaccount.

Example 5. The method of any of examples 1-4, wherein the providerreplica database account is a replica of the provider database account.

Example 6. The method of any of examples 1-5, wherein the providerreplica database account is not an exact replica and does not comprise acomplete version of the provider dataset.

Example 7. The method of any of examples 1-6, wherein the crossreference table cross references client end-users in the client datasetand provider end-users in the provider dataset.

Example 8. The method of any of examples 1-7, wherein the clientend-users and the provider-end user are cross referenced in the crossreference table using a shared end-user identifier as a cross referencekey.

Example 9. The method of any of examples 1-8, wherein the crossreference table anonymizes end-user identifiers without indicatingwhether each client end-user corresponds to a provider end-user.

Example 10. The method of any of examples 1-9, wherein at least oneclient end-user identifier is correlated, in the cross reference table,with a dummy value based on the provider dataset not having a providerend-user identifier that matches the at least one client end-useridentifier.

Example 11. The method of any of examples 1-10, wherein at least oneprovider end-user identifier is correlated, in the cross referencetable, with a dummy value based on the client dataset not having aclient end-user identifier that matches the at least one providerend-user identifier.

Example 12. The method of any of examples 1-11, wherein the firstvirtual cloud and the second virtual cloud are in different geographicregions.

Example 13. The method of any of examples 1-12, wherein the firstvirtual cloud and the second virtual cloud are virtual clouds ofdifferent private virtual cloud platforms.

Example 14. The method of any of examples 1-13, wherein the firstvirtual cloud and the second virtual cloud are in different geographicregions, and wherein the first virtual cloud is a first private virtualcloud platform and the second virtual cloud is a second virtual privatecloud platform.

Example 15. A system comprising: one or more processors of a machine;and a memory storing instructions that, when executed by the one or moreprocessors, cause the machine to perform operations implementing any oneof example methods 1 to 14.

Example 16. A machine-readable storage device embodying instructionsthat, when executed by a machine, cause the machine to performoperations implementing any one of example methods 1 to 14.

What is claimed is:
 1. A method performed by executing instructions onat least one hardware processor, the method comprising: accessing afirst dataset associated with a first database account; accessing asecond dataset associated with a second database account; generating aninterim table accessible by the first database account by applying afirst query to a table associated with the first dataset and the seconddataset; generating results data accessible by the second databaseaccount by applying the first query or a second query to the interimtable and the second dataset; and storing the results data accessible bythe first database account.
 2. The method of claim 1, wherein the tableassociated with the first and second dataset is a cross reference tablethat cross references the first dataset and the second dataset.
 3. Themethod of claim 2, further comprising generating the cross referencetable that cross references the first dataset and the second dataset. 4.The method of claim 2, wherein the cross reference table anonymouslycross references the first dataset and the second dataset.
 5. The methodof claim 3, wherein the cross reference table uses hashing toanonymously cross reference the first dataset and the second dataset. 6.The method of claim 3, wherein the cross reference table uses encryptionto anonymously cross reference the first dataset and the second dataset.7. The method of claim 3, wherein the cross reference table usestokenization to anonymously cross reference the first dataset and thesecond dataset.
 8. A computer system comprising: at least one hardwareprocessor; and one or more non-transitory computer readable storagemedia containing instructions that, when executed by the at least onehardware processor, cause the computer system to perform operationscomprising: accessing a first dataset associated with a first databaseaccount; accessing a second dataset associated with a second databaseaccount; generating an interim table accessible by the first databaseaccount by applying a first query to a table associated with the firstdataset and the second dataset; generating results data accessible bythe second database account by applying the first query or a secondquery to the interim table and the second dataset; and storing theresults data accessible by the first database account.
 9. The computersystem of claim 8, wherein generating the results data is based on theapplication of the first query to the interim table and the seconddataset.
 10. The computer system of claim 8, wherein generating theresults data is based on the application of the second query to theinterim table and the second dataset.
 11. The computer system of claim8, wherein the query is directed to a combination of the first datasetand the second dataset.
 12. The computer system of claim 8, whereingenerating the interim table is based on applying the first query to thesecond dataset.
 13. The computer system of claim 8, wherein the firstand second database accounts both reside in a networked databaseplatform.
 14. One or more non-transitory computer readable storage mediacontaining instructions that, when executed by at least one hardwareprocessor of a computer system, cause the computer system to performoperations comprising: accessing a first dataset associated with a firstdatabase account; accessing a second dataset associated with a seconddatabase account; generating an interim table accessible by the firstdatabase account by applying a first query to a table associated withthe first dataset and the second dataset; generating results dataaccessible by the second database account by applying the first query ora second query to the interim table and the second dataset; and storingthe results data accessible by the first database account.
 15. The oneor more non-transitory computer readable storage media of claim 14,wherein: the first database account resides in a first networkeddatabase platform; and the second database resides in a second networkeddatabase platform.
 16. The one or more non-transitory computer readablestorage media of claim 15, wherein the first networked database platformand the second networked database platform are in different geographicregions.
 17. The one or more non-transitory computer readable storagemedia of claim 15, further comprising generating a replica seconddatabase account in the first networked database platform, the replicasecond database account being a replica of the second database account,the replica second database account being managed by the second databaseaccount.
 18. The one or more non-transitory computer readable storagemedia of claim 17, wherein: the interim table is passed from the firstdatabase account to the second database account via the replica seconddatabase account; and the results data is passed from the seconddatabase account to the first database account via the replica seconddatabase account.
 19. The one or more non-transitory computer readablestorage media of claim 14, wherein the table associated with the firstand second dataset is a cross reference table that cross references thefirst dataset and the second dataset.
 20. The one or more non-transitorycomputer readable storage media of claim 19, further comprisinggenerating the cross reference table that cross references the firstdataset and the second dataset.